A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Journal of Cognitive Neuroscience
A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections
IEEE Transactions on Information Technology in Biomedicine
A new preprocessing approach for cell recognition
IEEE Transactions on Information Technology in Biomedicine
Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation
Image and Vision Computing
Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Multiclass detection of cells in multicontrast composite images
Computers in Biology and Medicine
Classification of mycobacterium tuberculosis in images of ZN-stained sputum smears
IEEE Transactions on Information Technology in Biomedicine
A visual targeting system for the microinjection of unstained adherent cells
Computers in Biology and Medicine
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Detection of unstained viable cells in bright field images is an inherently difficult task due to the immense variability of cell appearance. Traditionally, it has required human observers. However, in high-throughput robotic systems, an automatic procedure is essential. In this paper, we formulate viable cell detection as a supervised, binary pattern recognition problem and show that a support vector machine (SVM) with an improved training algorithm provides highly effective cell identification. In the case of cell detection, the binary classification problem generates two classes, one of which is much larger than the other. In addition, the total number of samples is extremely large. This combination represents a difficult problem for SVMs. We solved this problem with an iterative training procedure (''Compensatory Iterative Sample Selection'', CISS). This procedure, which was systematically studied under various class size ratios and overlap conditions, was found to outperform several commonly used methods, primarily owing to its ability to choose the most representative samples for the decision boundary. Its speed and accuracy are sufficient for use in a practical system.